Bayesian Indirect Comparison and Cost-Effectiveness of Sacituzumab Govitecan Versus Sacituzumab Tirumotecan in Metastatic Triple-Negative Breast Cancer.

[OBJECTIVES] Sacituzumab govitecan (SG) and sacituzumab tirumotecan (sac-TMT) are 2 approved antibody-drug conjugates for metastatic triple-negative breast cancer but lack direct comparison. Given their similar indications and high costs, an indirect cost-effectiveness analysis is essential to inform pricing and reimbursement decisions from the perspective of the Chinese healthcare system.

[METHODS] A partitioned survival model using Bayesian network meta-analysis and flexible parametric modeling estimated long-term survival, quality-adjusted life-years (QALYs), and costs. Participants were derived from the ASCENT and OptiTROP-Breast01 randomized controlled trials. Key outcomes included time-varying hazard ratio, life-years, costs, incremental cost-effectiveness ratio (ICER), incremental net health benefit, and incremental net monetary benefit. Multiple sensitivity and scenario analyses tested model robustness.

[RESULTS] In the base-case analysis, SG yielded 0.095 additional QALYs compared with sac-TMT at an incremental cost of $68 266, resulting in an ICER of $719 726 per QALY and an incremental net monetary benefit of -$64 400. Sensitivity analyses identified SG price and model time horizon as key drivers of ICER. In all plausible scenarios, SG remained not cost-effective versus sac-TMT. Probabilistic results showed a 0% probability of cost-effectiveness at a willingness-to-pay threshold of $40 763 per QALY. Scenario analysis confirmed the robustness of findings across assumptions on time horizon, utility, wastage, and discounting.

[CONCLUSIONS] SG showed slightly greater health gains but much higher costs than sac-TMT, leading to poor cost-effectiveness at current prices. Given the high substitutability between antibody-drug conjugates in metastatic triple-negative breast cancer, indirect comparisons with economic modeling can help guide payer decisions and pricing negotiations when head-to-head data are lacking.